def icele():
# 获取ec数据
dics = ['ECMWF_HR\SKINT', 'ECMWF_HR\APCP', 'ECMWF_HR\SNOD']
ectime = ecmwf.ecreptime()
fh = [i for i in range(12, 181, 3)
] # 20点的预报获取今天8:00的ec预报(此处可能存在问题，搞清12为5-8，还是8-11)
lonlatset, dataset = [], []
for dic in dics:
lon, lat, data = Datainterface.micapsdata(ectime, dic, fh)
lonlatset.append(lon, lat)
for i in range(data.shape[0] - 1):
# 此处参考233上的改动
newdata = []
if (np.isnan(data[i]).all() == True) and (i + 1 <= data.shape[0]):
data[i] = data[i + 1] / 2
data[i + 1] = data[i + 1] / 2
newdata.append(
interp.interpolateGridData(data[i], lat, lon, glovar.lat,
glovar.lon))
else:
newdata.append(
interp.interpolateGridData(data[i], lat, lon, glovar.lat,
glovar.lon))
dataset.append(np.array(newdata)) # 保存插值后的数据集,data形状应为（57，X, X）
return dataset
def Weatherdata(path):
# ?????????????????????
elements, subdirs, localdir, _, freq, *ftp = Writefile.readxml(path, 1)
now = datetime.datetime.now()
elements = elements.split(',')
subdirs = subdirs.split(',')
remote_urls = [os.path.join(subdir, now.strftime('%Y'), now.strftime('%Y%m%d')) for subdir in subdirs] # ??????
grib = Datainterface.GribData()
'''
[grib.mirror(element, remote_url, localdir, freq, ftp) for element, remote_url in
zip(elements[:-1], remote_urls[:-1])] # ???????³†??????????????????(24003)
'''
for element, remote_url in zip(elements[:-1], remote_urls[:-1]):
grib.mirror(element, remote_url, localdir, freq, ftp)
grib.mirror(elements[-1], remote_urls[-1], localdir, '24024', ftp) # ???????????
# ???????????????????§Ò????????????????pattern
strings = ','.join(os.listdir(localdir))
patterns = [r'(\w+.EDA.*?.GRB2)', r'(\w+.ERH.*?.GRB2)', r'(\w+.TMP.*?.GRB2)', r'(\w+.ER24.*?.GRB2)']
allpath = [localdir + sorted(Znwg.regex(pattern, strings), key=str.lower)[-1] for pattern in patterns] # allpath??????????????????§Ò?
ele14list = slice(1, 74, 8) # ??+2-1??????????¦Ä??10?????14?????????
####????????wind????u???v??
wind = grib.readGrib(allpath[0])[0]
windu_v = np.array([v for _, v in wind.items()])
windu, windv = windu_v[::2][ele14list], windu_v[1::2][ele14list]
data = np.array([Znwg.arrange(grib.readGrib(path))[0][ele14list] for path in allpath[1:-1]]) # ?????????????????
#er, lat, lon, size = Znwg.arrange(grib.readGrib(allpath[-1], nlat=glovar.lat, nlon=glovar.lon)) # ???????????????????¦Ã??????????????????
er, lat, lon, size = Znwg.arrange([grib.readGrib(allpath[-1], nlat=glovar.latt, nlon=glovar.lonn)][0])
result = windu, windv, data, er # ??????????[4,10,181,277]????
return result, lat, lon
def main():
saltedata = saltedata(path)
snowpre = np.random.randint(0, 1, size=(801 * 1381, 1))
snow = SnowDepth()
rep = ecmwf.ecreptime()
fh = [i for i in range(12, 181, 3)]
region = [
float(i) for i in ','.join(
Writefile.readxml(
r'/home/cqkj/QHTraffic/Product/Traffic/SNOD/config.xml',
0)).split(',')
]
new_lon = np.arange(region[0], region[2], region[-1])
new_lat = np.arange(region[1], region[3], region[-1])
lonlatset, dataset = [], []
# 提取数据及经纬度(双重循环，看能否改进)
for dic in snow.dics:
lon, lat, data = Datainterface.micapsdata(rep, dic, fh)
lonlatset.append((lon, lat))
for i in range(data.shape[0] - 1):
if (np.isnan(data[i]).all() == True) and (i + 1 <= data.shape[0]):
data[i] = data[i + 1] / 2
data[i + 1] = data[i + 1] / 2
interp.interpolateGridData(data, lat, lon, new_lat, new_lon)
else:
interp.interpolateGridData(data, lat, lon, new_lat, new_lon)
dataset.append(data) # 保存插值后的数据集
depthgrid = snow.clcsd(dataset, new_lat, new_lon, saltedata, snowpre)
snow.write(depthgrid, new_lat, new_lon)
dangerindex = snow.clcindex(depthgrid, new_lat, new_lon)
snow.write(dangerindex, type=1)
def presnow():
# 得到前一时刻积雪深度
ectime = ecmwf.ecreptime()
fh = [0]
dic = 'ECMWF_HR/SNOD'
lon, lat, data = Datainterface.micapsdata(ectime, dic, fh)
return interp.interpolateGridData(data, lat, lon, glovar.lat, glovar.lon)
def snowData():
# 获取ec数据信息(气温、降水、地温、湿度、积雪深度)
ectime = ecmwf.ecreptime()
fh = [i for i in range(12, 181, 3)] # 20点的预报获取今天8:00的ec预报
# *_, dics = Writefile.readxml(glovar.trafficpath, 0)
*_, dics = Writefile.readxml(
r'/home/cqkj/LZD/Product/Product/config/Traffic.xml', 0)
dicslist = dics.split(',')[:-1]
lonlatset, dataset = [], []
for dic in dicslist:
newdata = []
lon, lat, data = Datainterface.micapsdata(ectime, dic, fh)
lonlatset.append((lon, lat))
for i in range(data.shape[0] - 1):
if (np.isnan(data[i]).all() == True) and (i + 1 <= data.shape[0]):
data[i] = data[i + 1] / 2
data[i + 1] = data[i + 1] / 2
newdata.append(
interp.interpolateGridData(data[i], lat, lon, glovar.lat,
glovar.lon))
else:
newdata.append(
interp.interpolateGridData(data[i], lat, lon, glovar.lat,
glovar.lon))
newdata = np.array(newdata)
# newdata[newdata<0] = 0 # 保证数据正确性
dataset.append(newdata) # 保存插值后的数据集
return np.array(dataset)
def saltepath(path):
# 处理卫星数据，最后返回文件列表,path为卫星数据目录
if not os.path.exists(path):
print('The given path does not exist! Do not use modis data!')
return None
now = dt.datetime.now().strftime('%Y%m%d')
pattern = r'(\w' + now + '.*?tif)' # 匹配卫星文件名
strings = os.listdir(path)
tiflists = Znwg.regex(pattern, strings) # 返回当前卫星数据文件列表
if len(tiflists) == 0:
print(r'MODIS tiff don\'t exist, call model_2!')
return None
else:
print('MODIS exists, call model_1!')
gdal.AllRegister()
dataset = gdal.Open(tiflists)
rtf = Datainterface.ReadTiff(dataset)
px, py = rtf.imagexy2pro()
pro2geov = np.vectorize(rtf.pro2geo)
lon, lat = pro2geov(px, py) # 此处执行很慢，循环操作
# newlat =np.linspace(31.4,39.4,801)
# newlon =np.linspace(89.3,103.1,1381)
# *_, newdata = rtf.equalatlon('SnowDepth', dataset.ReadAsArray(), lat, lon, newlat, newlon)
*_, newdata = rtf.equalatlon('SnowDepth', dataset.ReadAsArray(), lat,
lon, glovar.lat, glovar.lon)
return newdata
def roadicgrid():
# 此程序用来同步cimiss站点数据
interfaceId = 'getSurfEleInRegionByTimeRange'
elements = "Station_Id_C,Lat,Lon,Road_ICE_Depth,Year,Mon,Day,Hour"
lastime = dt.datetime.now().replace(minute=0,
second=0) - dt.timedelta(hours=8)
fristime = lastime - dt.timedelta(hours=3)
temp = ('[', fristime.strftime('%Y%m%d%H%M%S'), ',',
lastime.strftime('%Y%m%d%H%M%S'), ')')
Time = ''.join(temp)
params = {
'dataCode': "SURF_CHN_TRAFW_MUL",
'elements': elements,
'timeRange': Time,
'adminCodes': "630000"
}
initnaldata = Datainterface.cimissdata(interfaceId, elements,
**params) # initnaldata 需先进行类型修改
initnaldata.Road_ICE_Depth = pd.to_numeric(initnaldata.Road_ICE_Depth)
initnaldata.Lat = pd.to_numeric(initnaldata.Lat)
initnaldata.Lon = pd.to_numeric(initnaldata.Lon)
initnaldata.reset_index(inplace=True)
o2tdata = cimissdata.onehour2threehour(initnaldata)
nonandata = cimissdata.cleandata(o2tdata, 999, 'Road_ICE_Depth')
#####################################################################
roadpath = r'E:\LZD\青海项目代码\qhroadic\eightroadmessage.pickle'
roadpath = r'/home/cqkj/LZD/Product/Product/Source/QHroad_update.csv'
roaddf = pd.read_csv(roadpath, index_col=0)
roaddf = readpickle(roadpath)
#####################################################################
roadvalue = cimissdata.insert2road(
nonandata, roaddf) # 得到插值后道路点的结冰数据，坐标则是有roaddf的lat、lon提供
roadvalue = pd.DataFrame(roadvalue)
# roadvalue.to_csv(r'C:\Users\GJW\Desktop\test.csv', index=None, mode=a)
cmpath = r'/home/cqkj/LZD/Product/Product/Source/iceday.pkl'
if not path.exists(cmpath):
dq = deque(roadvalue[np.newaxis, ...], maxlen=96)
with open(cmpath, 'wb') as f:
pickle.dump(dq, f)
else:
with open(cmpath, 'rb') as f:
dq = pickle.load(f)
dq.append(roadvalue[np.newaxis, ...])
with open(cmpath, 'wb') as f:
pickle.dump(dq, f)
return None
def Weatherdata(path):
# 获取森林火险所需气象数据
elements, subdirs, localdir, _, freq, *ftp = Writefile.readxml(path, 1)
now = datetime.datetime.now()
elements = elements.split(',')
subdirs = subdirs.split(',')
remote_urls = [
os.path.join(subdir, now.strftime('%Y'), now.strftime('%Y%m%d'))
for subdir in subdirs
] # 待构造
grib = Datainterface.GribData()
'''
[grib.mirror(element, remote_url, localdir, freq, ftp) for element, remote_url in
zip(elements[:-1], remote_urls[:-1])] # 同时同步大风、相对湿度、气温要素数据(24003)
'''
for element, remote_url in zip(elements[:-1], remote_urls[:-1]):
grib.mirror(element, remote_url, localdir, freq, ftp)
grib.mirror(elements[-1], remote_urls[-1], localdir, '24024',
ftp) # 同步出降水要素
# 此处应改为提取出不同要素列表，目前简单实现，构造四个pattern
strings = ','.join(os.listdir(localdir))
patterns = [
r'(\w+.EDA.*?.GRB2)', r'(\w+.ERH.*?.GRB2)', r'(\w+.TMP.*?.GRB2)',
r'(\w+.ER24.*?.GRB2)'
]
allpath = [
localdir + sorted(Znwg.regex(pattern, strings), key=str.lower)[-1]
for pattern in patterns
] # allpath应为四个最新同步的文件列表
ele14list = slice(1, 74, 8) # （+2-1）前三个要素未来10天每天14时数据索引
####第一个要素wind包含u风和v风
wind = grib.readGrib(allpath[0])[0]
windu_v = np.array([v for _, v in wind.items()])
windu, windv = windu_v[::2][ele14list], windu_v[1::2][ele14list]
data = np.array([
Znwg.arrange(grib.readGrib(path))[0][ele14list]
for path in allpath[1:-1]
]) # 读取出前三项数据信息
#er, lat, lon, size = Znwg.arrange(grib.readGrib(allpath[-1], nlat=glovar.lat, nlon=glovar.lon)) # 降水为国家级资料，先查看经纬度信息是否与前三者一致
er, lat, lon, size = Znwg.arrange(
[grib.readGrib(allpath[-1], nlat=glovar.latt, nlon=glovar.lonn)][0])
result = windu, windv, data, er # 最终数据应为[4,10,181,277]矩阵
return result, lat, lon
def liverain(path, pklpath):
# ???????ZNWG????????????ݴ?pickle
# ????????????????????????????
elements, _, localdir, historydir, freq, *ftp = Writefile.readxml(path, 1)
now = datetime.datetime.now()
ytd = now - datetime.timedelta(days=1)
dir = r'/SCMOC/BEXN'
remote_url = os.path.join(dir, ytd.strftime('%Y'), ytd.strftime('%Y%m%d'))
grb = Datainterface.GribData()
grb.mirror('ER24', remote_url, localdir, '24024', ftp) # ??????????????
rainpath = sorted(os.listdir(localdir))[-1]
os.chdir(localdir)
rainlive, lat, lon, res = Znwg.arrange([grb.readGrib(rainpath, nlat=glovar.latt, nlon=glovar.lonn)][0])
####??????????????????
with open(pklpath, 'rb') as f:
data = pickle.load(f)
data.append(rainlive)
# §Õ??deque????
with open(pklpath, 'wb') as f:
pickle.dump(data, f)
return rainlive